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Contract type: Full time (35 hours - 1FTE), fixed term (18 months) Detailed description The recruited candidate will be responsible for generating a database of qualitative data acquired through
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the decision analytic modelling field research field and contribute to high quality reports for funding bodies and peer-reviewed outputs. You will hold a DPhil/PhD in health economics or a related quantitative
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the decision analytic modelling field research field and contribute to high quality reports for funding bodies and peer-reviewed outputs. You will hold a DPhil/PhD in health economics or a related quantitative
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biology and targeted therapeutics Likelihood of advanced skills directly related to the research projects High level of analytical and problem-solving capability Ability to communicate complex information
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the programme code: 8080F Research area: Statistics Select ‘PhD Mathematics (Full Time)' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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) Applications are invited for a three-year PhD studentship. The studentship will start on1st Jan, 2026. Project Description Glioblastoma (GBM) is the most aggressive and treatment-resistant form of brain cancer
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an early warning system of dementia, to be exploited within existing healthcare pathways to trigger early intervention. Skills acquisition The studentship will provide training in data-analytics, statistics
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Closing Date Sunday 17 August 2025 Reference PHD002-25 A funded PhD position is available on a project funded by the Leverhulme Trust. Description below: The density of information content in screen media
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A funded PhD position is available on a project funded by the Leverhulme Trust. Description below: The density of information content in screen media – i.e., the light, volume and pitch changes from